




'''simple docstring'''


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_					=   logging.get_logger()





@dataclass
class  __lowercase    :
   _a    						=   42
   _a    						=   field(default_factory=__magic_name__			)
   _a    						=   field(default_factory=__magic_name__			)







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Any:
     __a								=       len(list(m.modules()   )   ) == 1 or isinstance(UpperCamelCase   ,				nn.Convad   ) or isinstance(UpperCamelCase   ,				nn.BatchNormad   )
     if has_not_submodules:
       self.traced.append(UpperCamelCase   )







   def __call__(						self   ,				UpperCamelCase   )					->     str:
     for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook   )   )
     self.module(UpperCamelCase   )
     [x.remove() for x in self.handles]
     return self







   @property
   def 		UpperCamelCase__      (						self   )					->     Any:
     # check the len of the state_dict keys to see if we have learnable params
     return list(filter(lambda UpperCamelCase   : len(list(x.state_dict().keys()   )   ) > 0   ,				self.traced   )   )





@dataclass
class  __lowercase    :
   _a    						=   42
   _a    						=   42
   _a    						=   0
   _a    						=   field(default_factory=__magic_name__			)
   _a    						=   field(default_factory=__magic_name__			)







   def __call__(						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Tracker(self.dest   )(UpperCamelCase   ).parametrized
     __a								=       Tracker(self.src   )(UpperCamelCase   ).parametrized

     __a								=       list(filter(lambda UpperCamelCase   : type(UpperCamelCase   ) not in self.src_skip   ,				UpperCamelCase   )   )
     __a								=       list(filter(lambda UpperCamelCase   : type(UpperCamelCase   ) not in self.dest_skip   ,				UpperCamelCase   )   )

     if len(UpperCamelCase   ) != len(UpperCamelCase   ):
       raise Exception(
           f"Numbers of operations are different. Source module has {len(UpperCamelCase   )} operations while"
           f" destination module has {len(UpperCamelCase   )}."   )

     for dest_m, src_m in zip(UpperCamelCase   ,				UpperCamelCase   ):
       dest_m.load_state_dict(src_m.state_dict()   )
       if self.verbose == 1:
         print(f"Transfered from={src_m} to={dest_m}"   )


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				ResNetConfig					,       a_						:				Path					,       a_						:				bool = True      ):
  print(f"Converting {name}..."      )
  with torch.no_grad():
    __a								=       timm.create_model(a_					,       pretrained=a_      ).eval()
    __a								=       ResNetForImageClassification(a_      ).eval()
    __a								=       ModuleTransfer(src=a_					,       dest=a_      )
    __a								=       torch.randn((1, 3, 224, 224)      )
    module_transfer(a_      )

  assert torch.allclose(from_model(a_      )					,       our_model(a_      ).logits      ), "The model logits don't match the original one."

  __a								=       f"resnet{'-'.join(name.split('resnet'      )      )}"
  print(a_      )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name					,       commit_message='Add model'					,       use_temp_dir=a_					,       )

    # we can use the convnext one
    __a								=       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name					,       commit_message='Add image processor'					,       use_temp_dir=a_					,       )

    print(f"Pushed {checkpoint_name}"      )


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				str = None					,       a_						:				bool = True      ):
  __a								=       'imagenet-1k-id2label.json'
  __a								=       1000
  __a								=       (1, num_labels)

  __a								=       'huggingface/label-files'
  __a								=       num_labels
  __a								=       json.load(open(hf_hub_download(a_					,       a_					,       repo_type='dataset'      )					,       'r'      )      )
  __a								=       {int(a_      ): v for k, v in idalabel.items()}

  __a								=       idalabel
  __a								=       {v: k for k, v in idalabel.items()}

  __a								=       partial(a_					,       num_labels=a_					,       idalabel=a_					,       labelaid=a_      )

  __a								=       {
      'resnet18': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]					,       hidden_sizes=[64, 128, 256, 512]					,       layer_type='basic'      ),
      'resnet26': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type='bottleneck'      ),
      'resnet34': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]					,       hidden_sizes=[64, 128, 256, 512]					,       layer_type='basic'      ),
      'resnet50': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type='bottleneck'      ),
      'resnet101': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type='bottleneck'      ),
      'resnet152': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type='bottleneck'      ),
  }

  if model_name:
    convert_weight_and_push(a_					,       names_to_config[model_name]					,       a_					,       a_      )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(a_					,       a_					,       a_					,       a_      )
  return config, expected_shape


if __name__ == "__main__":
  UpperCAmelCase_					=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default=None,
      type=str,
      help=(
          "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
          " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=Path,
      required=True,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument(
      "--push_to_hub",
      default=True,
      type=bool,
      required=False,
      help="If True, push model and image processor to the hub.",
  )

  UpperCAmelCase_					=   parser.parse_args()
  UpperCAmelCase_					=   args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''simple docstring'''


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'					,       [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Dict      ):
  __a								=       tmp_path_factory.mktemp('dset_infos_dir'      )
  if "full:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
  if "empty:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / 'dataset_infos.json'					,       'w'      ) as f:
      f.write('{"default": {"dataset_size": 42}}'      )
  __a								=       DatasetInfosDict.from_directory(a_      )
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    'dataset_info'					,       [
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				DatasetInfo      ):
  __a								=       str(a_      )
  dataset_info.write_to_directory(a_      )
  __a								=       DatasetInfo.from_directory(a_      )
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(a_					,       'dataset_info.json'      )      )


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo(
      description='foo'					,       citation='bar'					,       homepage='https://foo.bar'					,       license='CC0'					,       features=Features({'a': Value('int32'      )}      )					,       post_processed={}					,       supervised_keys=()					,       task_templates=[]					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train', 'num_examples': 42}]					,       download_checksums={}					,       download_size=1337					,       post_processing_size=442					,       dataset_size=1234					,       size_in_bytes=1337 + 442 + 1234					,       )
  __a								=       dataset_info._to_yaml_dict()
  assert sorted(a_      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]					,       (list, dict, int, str)      )
  __a								=       yaml.safe_dump(a_      )
  __a								=       yaml.safe_load(a_      )
  assert dataset_info_yaml_dict == reloaded


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo()
  __a								=       dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    'dataset_infos_dict'					,       [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       )
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42      ),
                'v2': DatasetInfo(dataset_size=1337      ),
            }      ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				DatasetInfosDict      ):
  __a								=       str(a_      )
  dataset_infos_dict.write_to_directory(a_      )
  __a								=       DatasetInfosDict.from_directory(a_      )

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    __a								=       config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    __a								=       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(a_					,       'README.md'      )      )



